doc/master/rf205__compplot_8py.html

import ROOT


# Set up composite pdf

# --------------------------------------


# Declare observable x

x = ROOT.RooRealVar("x", "x", 0, 10)


# Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and

# their parameters

mean = ROOT.RooRealVar("mean", "mean of gaussians", 5)

sigma1 = ROOT.RooRealVar("sigma1", "width of gaussians", 0.5)

sigma2 = ROOT.RooRealVar("sigma2", "width of gaussians", 1)

sig1 = ROOT.RooGaussian("sig1", "Signal component 1", x, mean, sigma1)

sig2 = ROOT.RooGaussian("sig2", "Signal component 2", x, mean, sigma2)


# Sum the signal components into a composite signal pdf

sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)

sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])


# Build Chebychev polynomial pdf

a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0.0, 1.0)

a1 = ROOT.RooRealVar("a1", "a1", -0.2, 0.0, 1.0)

bkg1 = ROOT.RooChebychev("bkg1", "Background 1", x, [a0, a1])


# Build expontential pdf

alpha = ROOT.RooRealVar("alpha", "alpha", -1)

bkg2 = ROOT.RooExponential("bkg2", "Background 2", x, alpha)


# Sum the background components into a composite background pdf

bkg1frac = ROOT.RooRealVar("bkg1frac", "fraction of component 1 in background", 0.8, 0.0, 1.0)

bkg = ROOT.RooAddPdf("bkg", "Total background", [bkg1, bkg2], [bkg1frac])


# Sum the composite signal and background

bkgfrac = ROOT.RooRealVar("bkgfrac", "fraction of background", 0.5, 0.0, 1.0)

model = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig], [bkgfrac])


# Set up basic plot with data and full pdf

# ------------------------------------------------------------------------------


# Generate a data sample of 1000 events in x from model

data = model.generate({x}, 1000)


# Plot data and complete PDF overlaid

xframe = x.frame(Title="Component plotting of pdf=(sig1+sig2)+(bkg1+bkg2)")

data.plotOn(xframe)

model.plotOn(xframe)


# Clone xframe for use below

xframe2 = xframe.Clone("xframe2")


# Make component by object reference

# --------------------------------------------------------------------


# Plot single background component specified by object reference

ras_bkg = {bkg}

model.plotOn(xframe, Components=ras_bkg, LineColor="r")


# Plot single background component specified by object reference

ras_bkg2 = {bkg2}

model.plotOn(xframe, Components=ras_bkg2, LineStyle="--", LineColor="r")


# Plot multiple background components specified by object reference

# Note that specified components may occur at any level in object tree

# (e.g bkg is component of 'model' and 'sig2' is component 'sig')

ras_bkg_sig2 = {bkg, sig2}

model.plotOn(xframe, Components=ras_bkg_sig2, LineStyle=":")


# Make component by name/regexp

# ------------------------------------------------------------


# Plot single background component specified by name

model.plotOn(xframe2, Components="bkg", LineColor="c")


# Plot multiple background components specified by name

model.plotOn(xframe2, Components="bkg1,sig2", LineStyle=":", LineColor="c")


# Plot multiple background components specified by regular expression on

# name

model.plotOn(xframe2, Components="sig*", LineStyle="--", LineColor="c")


# Plot multiple background components specified by multiple regular

# expressions on name

model.plotOn(xframe2, Invisible=True, Components="bkg1,sig*", LineStyle="--", LineColor="y")


# Draw the frame on the canvas

c = ROOT.TCanvas("rf205_compplot", "rf205_compplot", 800, 400)

c.Divide(2)

c.cd(1)

ROOT.gPad.SetLeftMargin(0.15)

xframe.GetYaxis().SetTitleOffset(1.4)

xframe.Draw()

c.cd(2)

ROOT.gPad.SetLeftMargin(0.15)

xframe2.GetYaxis().SetTitleOffset(1.4)

xframe2.Draw()


c.SaveAs("rf205_compplot.png")